OpendTect dGB Plugins User Documentation version 4.2

dGB Earth Sciences

Copyright © 2002-2011 by dGB Beheer B.V.

All rights reserved. No part of this publication may be reproduced and/or published by print, photo print, microfilm or any other means without the written consent of dGB Beheer B.V. Under the terms and conditions of either of the licenses holders are permitted to make hardcopies for internal use: Commercial agreement Academic agreement
Table of Contents
1. Introduction
2. Steering
2.1. Background
2.2. Create Steering Data
2.2.1. Import Steering Data
2.2.2. Create SteeringCube
2.2.2.1. Description
2.2.2.2. Create Steering Cube window
2.2.3. Filter
2.2.3.1. Description
2.2.3.2. Filter steering cube window
2.2.4. Display SteeringCube
2.3. Attributes with steering
2.3.1. Curvature
2.3.1.1. Mean Curvature
2.3.1.2. Gaussian Curvature
2.3.1.3. Maximum Curvature
2.3.1.4. Minimum Curvature
2.3.1.5. Most Positive Curvature
2.3.1.6. Most Negative Curvature
2.3.1.7. Shape Index
2.3.1.8. Dip Curvature
2.3.1.9. Strike Curvature
2.3.1.10. Contour Curvature
2.3.1.11. Curvedness
2.3.1.12. General Remark
2.3.2. Dip
2.3.2.1. Polar dip
2.3.2.2. Azimuth
2.3.2.3. Inline dip
2.3.2.4. Crossline dip
2.3.2.5. Apparent Dip
2.3.2.6. Line dip
2.3.3. Dip angle
2.3.4. Position
2.3.5. Reference shift
2.3.6. Similarity
2.3.7. Volume Statistics
2.4. Benchmark Steering Cube Creation
2.4.1. Speed vs. algorithm and calculation cube size
2.4.2. Visual quality check
2.4.3. Crossline dip attribute
2.4.4. Filtering of the steering cubes
2.4.5. Steered Similarity attribute
2.4.6. Choosing a steering algorithm
3. HorizonCube
3.1. Introduction
3.2. Data Preparation
3.2.1. Horizon preparation
3.2.1.1. Trimming Horizons at faults
3.2.2. Filtering the SteeringCube
3.2.3. Create 2D Seismic Lattice
3.3. Create/Edit Horizons from a SteeringCube
3.4. Create HorizonCube (2D/3D)
3.4.1. Model-driven settings
3.4.2. Data-driven settings
3.4.2.1. Advanced options
3.4.2.2. Continuous Events
3.4.2.3. Truncated Events
3.5. Display Properties for HorizonCube
3.6. Tools
3.6.1. Add More Iterations
3.6.2. Modify Packages / Re-calculate 3D Sequences
3.6.3. Extract Horizons
3.6.4. Convert HorizonCube to SteeringCube
3.6.5. Truncate HorizonCube
3.6.6. Get Continuous HorizonCube
3.6.7. Convert Chronostrat to HorizonCube
3.7. Preload a HorizonCube
3.8. HorizonCube Management
3.9. ASCII Export
3.10. Create Property Cubes
4. Sequence Stratigraphic Interpretation System (SSIS)
4.1. Introduction
4.1.1. SSIS Toolbar
4.2. Interpretation Window
4.2.1. Overview
4.2.2. Select/Define a Depositional Model
4.2.3. Interpretation Workflow
4.2.4. Display Systems Tracts
4.3. HorizonCube Slider
4.4. Wheeler Transform / Wheeler Scene
4.4.1. Wheeler Scene
4.4.2. Create Wheeler Output (2D/3D)
4.5. Flatten Horizon/Seismics
4.5.1. Flatten
4.5.2. Unflatten HorizonCube
4.6. Systems Tracts Attributes
4.7. Manual SSIS
5. Well Correlation Panel
5.1. Introduction
5.2. WCP Main Window
5.3. Correlation Displays and Settings
5.4. Pick Markers and Correlate
6. Neural Networks
6.1. Introduction
6.1.1. Supervised neural networks
6.1.2. Unsupervised neural networks
6.2. Neural Network Management Window
6.3. Neural network information
6.3.1. Supervised neural network information
6.3.2. Unsupervised neural network information
6.4. Import GDI networks window
6.5. New from PickSets
6.6. New from Well Data
6.6.1. Balance Data
6.6.2. NN Lithology codes
6.7. NN training window
6.7.1. Unsupervised training
6.7.1.1. Quick UVQ
6.7.1.2. Quality-based UVQ stacking
6.7.2. Supervised training from pickset
6.7.3. Supervised training from well data
7. Velocity Model Building
7.1. Introduction
7.2. Vertical Velocity Analysis
7.3. Horizon-based velocity update
7.4. Velocity display
7.4.1. VMB specific gridding step: gridding of velocity picks
7.4.2. VMB specific gridding step: Surface-limited filler
7.5. Input-Output
7.5.1. Pre-Stack events export
8. Common Contour Binning
8.1. Introduction
8.2. CCB Main window
8.3. CCB Analysis
8.4. LocalCCB attribute
9. Applications
9.1. How to Make TheChimneyCube®
9.1.1. Workflow
9.1.2. Picking example locations
9.1.3. Neural network training
9.1.4. Evaluation and application of the trained neural network
9.2. The Dip-Steered Median Filter
9.2.1. Example results
9.2.2. Create a Dip-Steered median filter
9.2.3. Note
10. Default attribute sets
10.1. Evaluate Attributes
10.2. dGB Evaluate Attributes
10.3. NN Chimney Cube
10.4. NN Fault Cube
10.5. NN FaultCube Advanced
10.6. NN Salt Cube
10.7. NN Slump Cube
10.8. Unsupervised Waveform Segmentation
10.9. Ridge-Enhancement Filter
10.10. Dip-Steered Median Filter
10.11. Dip-Steered Diffusion Filter
10.12. Fault Enhancement Filter
10.13. Fault Enhancement Attributes
10.14. Seismic Filters Median-Diffusion-Fault-Enhancement
11. References